Autonomous vehicle navigation is used in a variety of unmanned ground, underwater, and aerospace vehicles, such as robots and unmanned aerial vehicles. An autonomous vehicle is required to make decisions and respond to situations completely without human intervention. Navigation and control provide major limitations to the overall performance, accuracy, and robustness of an autonomous vehicle. In order to perform navigation properly, an autonomous vehicle must be able to sense its location, steer toward a desired destination, and avoid obstacles. An autonomous vehicle requires the integration of many sensors, actuators, and controllers to provide navigation and control.
Various modalities have been proposed or used to provide navigation of autonomous vehicles. These include use of the Global Positioning System (GPS), inertial measurements from sensors, and image measurements from cameras. The use of GPS, inertial, or image measurements alone are each problematic for autonomous vehicle navigation. For instance, the vehicle may not receive signals from enough GPS satellites to compute a unique position, or changes in the visible satellite configuration may cause large jumps in the estimated position. While the vehicle position can be estimated by integrating inertial measurements, these estimates accumulate large errors over time. Similarly, methods for computing vehicle positions from image measurements, such as shape-from-motion, also accumulate errors over time. These may include gross errors resulting from image matching errors or poor conditioning of the shape-from-motion estimation problem.
Some current autonomous vehicle navigation systems perform deep integration of GPS and navigation-grade inertial measurements for vehicle navigation. Other conventional navigation systems use deep integration of inertial measurements and image measurements for estimating the position of a vehicle or sensor rig over time. In other systems, navigation solutions are independently estimated from GPS, inertial, and/or image measurements, and then these estimates are merged into a single solution using an ad hoc method (not deep integration) such as averaging. These ad hoc systems typically use two of the three modalities.